/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.flink.api.scala.extensions.impl.acceptPartialFunctions

import org.apache.flink.annotation.PublicEvolving
import org.apache.flink.api.common.typeinfo.TypeInformation
import org.apache.flink.api.scala.{DataSet, GroupedDataSet}
import org.apache.flink.util.Collector

import scala.reflect.ClassTag

/**
 * Wraps a data set, allowing to use anonymous partial functions to perform extraction of items in a
 * tuple, case class instance or collection
 *
 * @param ds
 *   The wrapped data set
 * @tparam T
 *   The type of the data set items
 */
class OnDataSet[T](ds: DataSet[T]) {

  /**
   * Applies a function `fun` to each item of the data set
   *
   * @param fun
   *   The function to be applied to each item
   * @tparam R
   *   The type of the items in the returned data set
   * @return
   *   A dataset of R
   */
  @PublicEvolving
  def mapWith[R: TypeInformation: ClassTag](fun: T => R): DataSet[R] =
    ds.map(fun)

  /**
   * Applies a function `fun` to a partition as a whole
   *
   * @param fun
   *   The function to be applied on the whole partition
   * @tparam R
   *   The type of the items in the returned data set
   * @return
   *   A dataset of R
   */
  @PublicEvolving
  def mapPartitionWith[R: TypeInformation: ClassTag](fun: Stream[T] => R): DataSet[R] =
    ds.mapPartition((it: Iterator[T], out: Collector[R]) => out.collect(fun(it.toStream)))

  /**
   * Applies a function `fun` to each item of the dataset, producing a collection of items that will
   * be flattened in the resulting data set
   *
   * @param fun
   *   The function to be applied to each item
   * @tparam R
   *   The type of the items in the returned data set
   * @return
   *   A dataset of R
   */
  @PublicEvolving
  def flatMapWith[R: TypeInformation: ClassTag](fun: T => TraversableOnce[R]): DataSet[R] =
    ds.flatMap(fun)

  /**
   * Applies a predicate `fun` to each item of the data set, keeping only those for which the
   * predicate holds
   *
   * @param fun
   *   The predicate to be tested on each item
   * @return
   *   A dataset of R
   */
  @PublicEvolving
  def filterWith(fun: T => Boolean): DataSet[T] =
    ds.filter(fun)

  /**
   * Applies a reducer `fun` to the data set
   *
   * @param fun
   *   The reducing function to be applied on the whole data set
   * @return
   *   A data set of Rs
   */
  @PublicEvolving
  def reduceWith(fun: (T, T) => T): DataSet[T] =
    ds.reduce(fun)

  /**
   * Applies a reducer `fun` to a grouped data set
   *
   * @param fun
   *   The function to be applied to the whole grouping
   * @tparam R
   *   The type of the items in the returned data set
   * @return
   *   A dataset of Rs
   */
  @PublicEvolving
  def reduceGroupWith[R: TypeInformation: ClassTag](fun: Stream[T] => R): DataSet[R] =
    ds.reduceGroup((it: Iterator[T], out: Collector[R]) => out.collect(fun(it.toStream)))

  /**
   * Groups the items according to a grouping function `fun`
   *
   * @param fun
   *   The grouping function
   * @tparam K
   *   The return type of the grouping function, for which type information must be known
   * @return
   *   A grouped data set of Ts
   */
  @PublicEvolving
  def groupingBy[K: TypeInformation](fun: T => K): GroupedDataSet[T] =
    ds.groupBy(fun)

}
